Delayed onsets are not necessary for generating distractor quitting thresholds effects in visual search
Bibliographic record
Abstract
Salient distractors lower quitting thresholds in visual search. That is, when searching for the presence of a target among filler items, a large heterogeneously coloured distractor presented at a delayed onset produces quick target-absent judgements and increased target-present errors. The aim of the current study was to explore if the timing of the salient distractor modulates this Quitting Threshold Effect (QTE). In Experiment 1, participants completed a target detection search task in the presence or absence of a salient singleton distractor that either appeared simultaneously with other search items or appeared at a delayed onset (i.e., 100 ms or 250 ms after other array items appeared). In Experiment 2, a similar method was used, except that the salient singleton distractor appeared simultaneously, 100 ms before, or 100 ms after the other array items. Across both experiments, we observed robust distractor QTEs. Regardless of their onset, salient distractors decreased target-absent search speeds and increased target-present error rates. In all, the present findings suggest that delayed onsets are not required for lowered quitting thresholds in visual search.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".